Abstract: Computations with higher than the IEEE 754 standard double-precision (about 16 significant digits) are required recently. Although there are available software routines in Fortran and C for high-precision computation, users are required to implement such routines in their own computers with detailed knowledges about them. We have constructed an user-friendly online system for octupleprecision computation. In our Web system users with no knowledges about high-precision computation can easily perform octupleprecision computations, by choosing mathematical functions with argument(s) inputted, by writing simple mathematical expression(s) or by uploading C program(s). In this paper we enhance the Web system above by adding the facility of uploading Fortran programs, which have been widely used in scientific computing. To this end we construct converter routines in two stages.
Abstract: In this paper we present a method for gene ranking
from DNA microarray data. More precisely, we calculate the correlation
networks, which are unweighted and undirected graphs, from
microarray data of cervical cancer whereas each network represents
a tissue of a certain tumor stage and each node in the network
represents a gene. From these networks we extract one tree for
each gene by a local decomposition of the correlation network. The
interpretation of a tree is that it represents the n-nearest neighbor
genes on the n-th level of a tree, measured by the Dijkstra distance,
and, hence, gives the local embedding of a gene within the correlation
network. For the obtained trees we measure the pairwise similarity
between trees rooted by the same gene from normal to cancerous
tissues. This evaluates the modification of the tree topology due to
progression of the tumor. Finally, we rank the obtained similarity
values from all tissue comparisons and select the top ranked genes.
For these genes the local neighborhood in the correlation networks
changes most between normal and cancerous tissues. As a result
we find that the top ranked genes are candidates suspected to be
involved in tumor growth and, hence, indicates that our method
captures essential information from the underlying DNA microarray
data of cervical cancer.
Abstract: It is known that if harmonic spectra are decreased, then
acoustic noise also decreased. Hence, this paper deals with a new
random switching strategy using DSP TMS320F2812 to decrease the
harmonics spectra of single phase switched reluctance motor. The
proposed method which combines random turn-on, turn-off angle
technique and random pulse width modulation technique is shown. A
harmonic spread factor (HSF) is used to evaluate the random
modulation scheme. In order to confirm the effectiveness of the new
method, the experimental results show that the harmonic intensity of
output voltage for the proposed method is better than that for
conventional methods.
Abstract: Environmental factors affect agriculture production
productivity and efficiency resulted in changing of profit efficiency.
This paper attempts to estimate the impacts of environmental factors
to profitability of rice farmers in the Red River Delta of Vietnam. The
dataset was extracted from 349 rice farmers using personal
interviews. Both OLS and MLE trans-log profit functions were used
in this study. Five production inputs and four environmental factors
were included in these functions. The estimation of the stochastic
profit frontier with a two-stage approach was used to measure
profitability. The results showed that the profit efficiency was about
75% on the average and environmental factors change profit
efficiency significantly beside farm specific characteristics. Plant
disease, soil fertility, irrigation apply and water pollution were the
four environmental factors cause profit loss in rice production. The
result indicated that farmers should reduce household size, farm
plots, apply row seeding technique and improve environmental
factors to obtain high profit efficiency with special consideration is
given for irrigation water quality improvement.
Abstract: We present a new method to reconstruct a temporally
coherent 3D animation from single or multi-view RGB-D video data
using unbiased feature point sampling. Given RGB-D video data, in
form of a 3D point cloud sequence, our method first extracts feature
points using both color and depth information. In the subsequent
steps, these feature points are used to match two 3D point clouds in
consecutive frames independent of their resolution. Our new motion
vectors based dynamic alignement method then fully reconstruct
a spatio-temporally coherent 3D animation. We perform extensive
quantitative validation using novel error functions to analyze the
results. We show that despite the limiting factors of temporal and
spatial noise associated to RGB-D data, it is possible to extract
temporal coherence to faithfully reconstruct a temporally coherent
3D animation from RGB-D video data.
Abstract: This research sought to discover the forms of
promotion and dissemination of traditional local wisdom that are
used to create occupations among the elderly at Noanmueng
Community, Muang Sub-District, Baan Doong District, Udornthani
Province. The criteria used to select the research sample group were:
having a role involved in the promotion and dissemination of
traditional local wisdom to create occupations among the elderly;
being an experienced person who the residents of Noanmueng
Community find trustworthy; and having lived in Noanmueng
Community for a long time so as to be able to see the development
and change that occurs. A total of 16 persons were thus selected. Data
was gathered through a qualitative study, using semi-structured indepth
interviews. The collected data was then summarized and
discussed according to the research objectives. Finally, the data was
presented in narrative format. Results found that the identifying
traditional local wisdom of the community (which grew from the
residents’ experience and beneficial usage in daily life, passed down
from generation to generation) was the weaving of cloth and
basketry. As for the manner of promotion and dissemination of
traditional local wisdom, these skills were passed down through
teaching by example to family members, relatives and others in the
community. This was largely the initiative of the elders or elderly
members of the community. In order for the promotion and
dissemination of traditional local wisdom to create occupations
among the elderly, the traditional local wisdom should be supported
in every way through participation of the community members. For
example, establish a museum of traditional local wisdom for the
collection of traditional local wisdom in various fields, both from the
past and present innovations. This would be a source of pride for the
community, simultaneously helping traditional local wisdom to
become widely known and to create income for the community’s
elderly. Additional ways include organizing exhibitions of products
made by traditional local wisdom, finding both domestic and
international markets, as well as building both domestic and
international networks aiming to find opportunities to market
products made by traditional local wisdom.
Abstract: Studies in neuroscience suggest that both global and
local feature information are crucial for perception and recognition of
faces. It is widely believed that local feature is less sensitive to
variations caused by illumination, expression and illumination. In
this paper, we target at designing and learning local features for face
recognition. We designed three types of local features. They are
semi-global feature, local patch feature and tangent shape feature.
The designing of semi-global feature aims at taking advantage of
global-like feature and meanwhile avoiding suppressing AdaBoost
algorithm in boosting weak classifies established from small local
patches. The designing of local patch feature targets at automatically
selecting discriminative features, and is thus different with traditional
ways, in which local patches are usually selected manually to cover
the salient facial components. Also, shape feature is considered in
this paper for frontal view face recognition. These features are
selected and combined under the framework of boosting algorithm
and cascade structure. The experimental results demonstrate that the
proposed approach outperforms the standard eigenface method and
Bayesian method. Moreover, the selected local features and
observations in the experiments are enlightening to researches in
local feature design in face recognition.
Abstract: Based on the feature of model disturbances and uncertainty being compensated dynamically in auto – disturbances-rejection-controller (ADRC), a new method using ADRC is proposed for the decoupling control of dispenser longitudinal movement in big flight envelope. Developed from nonlinear model directly, ADRC is especially suitable for dynamic model that has big disturbances. Furthermore, without changing the structure and parameters of the controller in big flight envelope, this scheme can simplify the design of flight control system. The simulation results in big flight envelope show that the system achieves high dynamic performance, steady state performance and the controller has strong robustness.
Abstract: Fuel and oxidant gas delivery plate, or fuel cell
plate, is a key component of a Proton Exchange Membrane (PEM)
fuel cell. To manufacture low-cost and high performance fuel cell
plates, advanced computer modeling and finite element structure
analysis are used as virtual prototyping tools for the optimization
of the plates at the early design stage. The present study examines
thermal stress analysis of the fuel cell plates that are produced
using a patented, low-cost fuel cell plate production technique
based on screen-printing. Design optimization is applied to
minimize the maximum stress within the plate, subject to strain
constraint with both geometry and material parameters as design
variables. The study reveals the characteristics of the printed
plates, and provides guidelines for the structure and material design
of the fuel cell plate.
Abstract: All practical real-time scheduling algorithms in multiprocessor systems present a trade-off between their computational complexity and performance. In real-time systems, tasks have to be performed correctly and timely. Finding minimal schedule in multiprocessor systems with real-time constraints is shown to be NP-hard. Although some optimal algorithms have been employed in uni-processor systems, they fail when they are applied in multiprocessor systems. The practical scheduling algorithms in real-time systems have not deterministic response time. Deterministic timing behavior is an important parameter for system robustness analysis. The intrinsic uncertainty in dynamic real-time systems increases the difficulties of scheduling problem. To alleviate these difficulties, we have proposed a fuzzy scheduling approach to arrange real-time periodic and non-periodic tasks in multiprocessor systems. Static and dynamic optimal scheduling algorithms fail with non-critical overload. In contrast, our approach balances task loads of the processors successfully while consider starvation prevention and fairness which cause higher priority tasks have higher running probability. A simulation is conducted to evaluate the performance of the proposed approach. Experimental results have shown that the proposed fuzzy scheduler creates feasible schedules for homogeneous and heterogeneous tasks. It also and considers tasks priorities which cause higher system utilization and lowers deadline miss time. According to the results, it performs very close to optimal schedule of uni-processor systems.
Abstract: This paper deals with a numerical analysis of the
transient response of composite beams with strain rate dependent
mechanical properties by use of a finite difference method. The
equations of motion based on Timoshenko beam theory are derived.
The geometric nonlinearity effects are taken into account with von
Kármán large deflection theory. The finite difference method in
conjunction with Newmark average acceleration method is applied to
solve the differential equations. A modified progressive damage
model which accounts for strain rate effects is developed based on
the material property degradation rules and modified Hashin-type
failure criteria and added to the finite difference model. The
components of the model are implemented into a computer code in
Mathematica 6. Glass/epoxy laminated composite beams with
constant and strain rate dependent mechanical properties under
dynamic load are analyzed. Effects of strain rate on dynamic
response of the beam for various stacking sequences, load and
boundary conditions are investigated.
Abstract: Today we tend to go back to the past to our root
relation to nature. Therefore in search of friendly spaces there are
elements of natural environment introduced as elements of spatial
composition. Though reinvented through the use of the new
substance such as greenery, water etc. made possible by state of the
art technologies, still, in principal, they remain the same. As a result,
sustainable design, based upon the recognized means of composition
in addition to the relation of architecture and urbanism vs. nature
introduces a new aesthetical values into architectural and urban
space.
Abstract: Biometric techniques are gaining importance for
personal authentication and identification as compared to the
traditional authentication methods. Biometric templates are
vulnerable to variety of attacks due to their inherent nature. When a
person-s biometric is compromised his identity is lost. In contrast to
password, biometric is not revocable. Therefore, providing security
to the stored biometric template is very crucial. Crypto biometric
systems are authentication systems, which blends the idea of
cryptography and biometrics. Fuzzy vault is a proven crypto
biometric construct which is used to secure the biometric templates.
However fuzzy vault suffer from certain limitations like nonrevocability,
cross matching. Security of the fuzzy vault is affected
by the non-uniform nature of the biometric data. Fuzzy vault when
hardened with password overcomes these limitations. Password
provides an additional layer of security and enhances user privacy.
Retina has certain advantages over other biometric traits. Retinal
scans are used in high-end security applications like access control to
areas or rooms in military installations, power plants, and other high
risk security areas. This work applies the idea of fuzzy vault for
retinal biometric template. Multimodal biometric system
performance is well compared to single modal biometric systems.
The proposed multi modal biometric fuzzy vault includes combined
feature points from retina and fingerprint. The combined vault is
hardened with user password for achieving high level of security.
The security of the combined vault is measured using min-entropy.
The proposed password hardened multi biometric fuzzy vault is
robust towards stored biometric template attacks.
Abstract: Heat powered solid sorption is a feasible alternative to
electrical vapor compression refrigeration systems. In this paper,
activated carbon (powder type Maxsorb and fiber type ACF-A10)-
CO2 based adsorption cooling cycles are studied using the pressuretemperature-
concentration (P-T-W) diagram. The specific cooling
effect (SCE) and the coefficient of performance (COP) of these two
cooling systems are simulated for the driving heat source
temperatures ranging from 30 ºC to 90 ºC in terms of different
cooling load temperatures with a cooling source temperature of 25
ºC. It is found from the present analysis that Maxsorb-CO2 couple
shows higher cooling capacity and COP. The maximum COPs of
Maxsorb-CO2 and ACF(A10)-CO2 based cooling systems are found
to be 0.15 and 0.083, respectively. The main innovative feature of
this cooling cycle is the ability to utilize low temperature waste heat
or solar energy using CO2 as the refrigerant, which is one of the best
alternative for applications where flammability and toxicity are not
allowed.
Abstract: C-control chart assumes that process nonconformities follow a Poisson distribution. In actuality, however, this Poisson distribution does not always occur. A process control for semiconductor based on a Poisson distribution always underestimates the true average amount of nonconformities and the process variance. Quality is described more accurately if a compound Poisson process is used for process control at this time. A cumulative sum (CUSUM) control chart is much better than a C control chart when a small shift will be detected. This study calculates one-sided CUSUM ARLs using a Markov chain approach to construct a CUSUM control chart with an underlying Poisson-Gamma compound distribution for the failure mechanism. Moreover, an actual data set from a wafer plant is used to demonstrate the operation of the proposed model. The results show that a CUSUM control chart realizes significantly better performance than EWMA.
Abstract: This paper evaluates performances of an adaptive noise
cancelling (ANC) based target detection algorithm on a set of real test
data supported by the Defense Evaluation Research Agency (DERA
UK) for multi-target wideband active sonar echolocation system. The
hybrid algorithm proposed is a combination of an adaptive ANC
neuro-fuzzy scheme in the first instance and followed by an iterative
optimum target motion estimation (TME) scheme. The neuro-fuzzy
scheme is based on the adaptive noise cancelling concept with the
core processor of ANFIS (adaptive neuro-fuzzy inference system) to
provide an effective fine tuned signal. The resultant output is then
sent as an input to the optimum TME scheme composed of twogauge
trimmed-mean (TM) levelization, discrete wavelet denoising
(WDeN), and optimal continuous wavelet transform (CWT) for
further denosing and targets identification. Its aim is to recover the
contact signals in an effective and efficient manner and then determine
the Doppler motion (radial range, velocity and acceleration) at very
low signal-to-noise ratio (SNR). Quantitative results have shown that
the hybrid algorithm have excellent performance in predicting targets-
Doppler motion within various target strength with the maximum
false detection of 1.5%.
Abstract: Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characteristics. The procedure of proposed control chart pattern recognizer comprises three stages. First, multi-resolution wavelet analysis is used to generate time-shape and time-frequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bi-directional Kohonen network to make reduced and robust information. Third, a back-propagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.
Abstract: Centrally controlled authentication and authorization services can provide enterprise with an increase in security, more flexible access control solutions and an increased users' trust. By using redirections, users of all Web-based applications within an organization are authenticated at a single well known and secure Web site and using secure communication protocol. Users are first authenticated at the central server using their domain wide credentials before being redirected to a particular Web-based application. The central authentication server will then provide others with pertinence authorization related particulars and credentials of the authenticated user to the specific application. The trust between the clients and the server hosts is established by secure session keys exchange. Case- studies are provided to demonstrate the usefulness and flexibility of the proposed solution.
Abstract: This paper proposes a specialized Web robot to automatically collect objectionable Web contents for use in an objectionable Web content classification system, which creates the URL database of objectionable Web contents. It aims at shortening the update period of the DB, increasing the number of URLs in the DB, and enhancing the accuracy of the information in the DB.
Abstract: This paper introduces new algorithms (Fuzzy relative
of the CLARANS algorithm FCLARANS and Fuzzy c Medoids
based on randomized search FCMRANS) for fuzzy clustering of
relational data. Unlike existing fuzzy c-medoids algorithm (FCMdd)
in which the within cluster dissimilarity of each cluster is minimized
in each iteration by recomputing new medoids given current
memberships, FCLARANS minimizes the same objective function
minimized by FCMdd by changing current medoids in such away
that that the sum of the within cluster dissimilarities is minimized.
Computing new medoids may be effected by noise because outliers
may join the computation of medoids while the choice of medoids in
FCLARANS is dictated by the location of a predominant fraction of
points inside a cluster and, therefore, it is less sensitive to the
presence of outliers. In FCMRANS the step of computing new
medoids in FCMdd is modified to be based on randomized search.
Furthermore, a new initialization procedure is developed that add
randomness to the initialization procedure used with FCMdd. Both
FCLARANS and FCMRANS are compared with the robust and
linearized version of fuzzy c-medoids (RFCMdd). Experimental
results with different samples of the Reuter-21578, Newsgroups
(20NG) and generated datasets with noise show that FCLARANS is
more robust than both RFCMdd and FCMRANS. Finally, both
FCMRANS and FCLARANS are more efficient and their outputs
are almost the same as that of RFCMdd in terms of classification
rate.